A Normative-Prescriptive-Descriptive Approach to Analyzing CSP Heuristics

This paper presents a general framework for analyzing heuristics for constraint solving, including backtracking and arc consistency algorithms. It will emphasize heuristics for variable selection during search, since this is where major differences are found. In earlier work two basic approaches to this problem were developed. The first was a general theoretical framework for different types of heuristics, which characterized ideal performance so that the actual performance of heuristics could be compared to this standard. The second involved the discovery that, while there are a large number of features that can be used for heuristic decisions in variable ordering, differences in effectiveness boil down to only two basic “heuristic actions”. The present paper applies basic ideas from decision analysis to characterize these two approaches to better understand their status and interrelations. It shows that the first is essentially a normative decision analysis, and that models of this sort imply general prescriptive principles (notably the Fail-First Principle). The second is concerned with descriptive models of actual performance.